Apologies if this question has been asked before but I haven't come across any so far.
I have been experimenting with Autoencoders using Keras and Theano as my back end based on the tutorial from this tutorial which has been going on well so far. So I have a high-dimensional (over 15K features) dataset as examples with no labels and I would like to reduce these into some manageable features which I presume are in the hidden layer of an Autoencoder neural network. My question, therefore, is how would one extract the input features that are contributing to each of features in the hidden layers as I would like to use these for further analysis.
Thanks in advance